Introduction This analysis runs a simple procedure that identifies variables significantly different across sample groups via ANOVA and then plots a heatmap of these variables.
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Comparison between cell lines from 9 different cancer tissues (NCI-60); GSE5949
Reinhold WC, Reimers MA, Lorenzi P, Ho J et al. Multifactorial regulation of E-cadherin expression: an integrative study. Mol Cancer Ther 2010 Jan;9(1):1-16. PMID: 20053763.
Comparison between cell lines from 9 different cancer tissue of origin types (Breast, Central Nervous System, Colon, Leukemia, Melanoma, Non-Small Cell Lung, Ovarian, Prostate, Renal) from NCI-60 panel
The input data matrix has
Table 1. The mean, standard deviation, and range of all variables.
| Min. | 1st Qu. | Median | Mean | 3rd Qu. | Max. | |
|---|---|---|---|---|---|---|
| Mean | 2.2745000 | 4.3560000 | 5.6256667 | 5.6818881 | 6.8808333 | 12.162667 |
| SD | 0.5000402 | 0.5625195 | 0.6536183 | 0.7684433 | 0.8506456 | 3.108248 |
| Range | 2.0100000 | 2.7500000 | 3.2900000 | 3.6183349 | 4.2000000 | 8.840000 |
Run 1-way ANOVA on each variable to identify those significantly different across all sample groups.
Figure 1. Distribution of ANOVA p values. Number of variables with p values within each 0.01 interval.
Significant variables were selected using the following criteria:
As a result, 396 variables were selected. Click here to view these variables.
Figure 2. Color-coded data of selected variables different across sample groups (red = higher). Variables (rows) were clustered based on their correlation to each other and samples were arranged by groups.
Check out the RoCA home page for more information.
To reproduce this report:
Find the data analysis template you want to use and an example of its pairing YAML file here and download the YAML example to your working directory
To generate a new report using your own input data and parameter, edit the following items in the YAML file:
Run the code below within R Console or RStudio, preferablly with a new R session:
if (!require(devtools)) { install.packages('devtools'); require(devtools); }
if (!require(RCurl)) { install.packages('RCurl'); require(RCurl); }
if (!require(RoCA)) { install_github('zhezhangsh/RoCAR'); require(RoCA); }
CreateReport(filename.yaml); # filename.yaml is the YAML file you just downloaded and edited for your analysis
If there is no complaint, go to the output folder and open the index.html file to view report.
## R version 3.5.1 (2018-07-02)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS High Sierra 10.13.6
##
## Matrix products: default
## BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] gplots_3.0.1 htmlwidgets_1.3 DT_0.5
## [4] awsomics_0.0.0.9000 yaml_2.2.0 rmarkdown_1.10
## [7] knitr_1.20 RoCA_0.0.0.9000 RCurl_1.95-4.11
## [10] bitops_1.0-6 usethis_1.4.0 devtools_2.0.1
##
## loaded via a namespace (and not attached):
## [1] Rcpp_1.0.0 later_0.7.5 highr_0.7
## [4] compiler_3.5.1 prettyunits_1.0.2 base64enc_0.1-3
## [7] remotes_2.0.2 tools_3.5.1 digest_0.6.18
## [10] pkgbuild_1.0.2 pkgload_1.0.2 jsonlite_1.5
## [13] memoise_1.1.0 evaluate_0.12 rlang_0.3.0.1
## [16] shiny_1.2.0 cli_1.0.1 rstudioapi_0.8
## [19] crosstalk_1.0.0 withr_2.1.2 stringr_1.3.1
## [22] caTools_1.17.1.1 gtools_3.8.1 desc_1.2.0
## [25] fs_1.2.6 rprojroot_1.3-2 glue_1.3.0
## [28] R6_2.3.0 processx_3.2.0 sessioninfo_1.1.1
## [31] gdata_2.18.0 callr_3.0.0 magrittr_1.5
## [34] promises_1.0.1 backports_1.1.2 ps_1.2.1
## [37] htmltools_0.3.6 assertthat_0.2.0 xtable_1.8-3
## [40] mime_0.6 httpuv_1.4.5 KernSmooth_2.23-15
## [43] stringi_1.2.4 crayon_1.3.4
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